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From Old Texts to Modern Spellings: an Experiment in Automatic Normalisation

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From Old Texts to Modern Spellings: an Experiment in Automatic Normalisation Iris Hendrickx, Rita Marquilhas From old texts to modern spellings: an experiment in automatic normalisation We aim to tackle the problem of spelling variations in a corpus of personal Portugese letters from the 16th to the 20th century. We investigated the extent to which the task of normalising Portuguese spelling can be accom- plished automatically. We adapted VARD2 (Baron and Rayson, 2008), a statistical tool for normalising spelling, for use with the Portuguese language and studied its performance over four different time periods. Our results showed that VARD2 performed best on the older letters and worst on the most modern ones. In an extrinsic evaluation, we measured the usefulness of automatic normalisation for the linguistic task of automatic POS-tagging and showed that automatic normalisation of spelling helps improve the performance of the POS-tagger. 1 Introduction Our goal was to reduce the problem of spelling variations in the Portuguese CARDS-FLY corpus of personal letters written over a period of approximately 500 years, from the 16th to the 20th century. As the letters were written by a diverse group of authors, some of whom were semi-illiterate, and most of the manuscripts predate the first standardisation of Portuguese spelling which only took place in 1911, they contain many spelling variations. We wanted to make the corpus available for further linguistic research and also make it accessible to a wider community. As the corpus is being prepared for research into language change, given the excep- tional, near-spoken status of the texts, it was advisable to preserve the original spellings in a paleographic edition appropriate for research into sound variations and change, and for discourse analysis focussing on the specific behaviour of the social agents. However, a standardised corpus is essential for lexical and grammatical research, since this is the only format that can be given the necessary mark-up, such as POS tagging, semantic tagging, and syntactic parsing, enabling it to be used as an empirical tool for testing theories of lexical and syntactic change. On the other hand, the rarity of the documents in question also make them valuable to the lay public, since they represent part of the country’s cultural heritage, reflecting the everyday lives of ordinary people, especially those who suffered hardship. The 16th to 19th century corpus comprises original epistolary texts produced by servants, children, wives, lovers, thieves, soldiers, artisans, priests, political campaigners and many other social agents who fell foul of the Inquisition and the civil courts, two institutions that habitually seized personal correspondence for use as evidence. The letters in the JLCL 2011 – Band 26 (2) – 65-76 Hendrickx, Marquilhas 20th century corpus come from personal collections compiled by the families of former soldiers, emigrants, and political prisoners. Editions intended for the lay public have the understandable obligation to provide readers with a clean text, free of distracting variations in spelling. Our aim was therefore to add an additional orthographic component to the historical documents, which involved modernising the spelling. Modernising or normalising spelling is not an arbitrary step: if done manually it involves an enormous workload that can only be carried out by a specialist in historical linguistics and if done automatically, errors inevitably occur. Here we investigate the extent to which the task of modernising Portuguese spelling can be accomplished automatically. We adapted VARD2 (Baron and Rayson, 2008) a well-studied statistical tool for normalising spelling, for use with the Portuguese language and studied its performance over four different time periods. The performance of this automatic normalisation tool was evaluated using intrinsic and extrinsic methods. Firstly, the automatically normalised text was compared with manually normalised text. Secondly, as an evaluation of use, the effect and usefulness of automatic spelling normalisation was evaluated in terms of the task of automatic grammatical tagging. The performance of a POS-tagger used on a data set with the original, non- standardised spelling was compared with the effect of automatically normalised text and, as the upper bound, on manually normalised text. This research is similar to the work of Rayson et al. (2007) who investigated the usefulness of pre-processing with VARD2 for POS-tagging historical English and showed that normalisation does help to improve the performance of the POS-tagger. The paper is structured as follows. In the next section we first discuss previous approaches to historical spelling normalisation. In Section 3 the experimental setup is explained, in particular the adaptation of the VARD2 tool to Portuguese in Section 3.1. Section 3.2 describes the historical corpus and provides additional background information on historical spelling changes in Portuguese. In Sections 3.3 and 5 the results of the experiments in normalisation and POS-tagging are presented and the conclusion appears in 6. 2 Related work The problem of spelling variations in historical texts has been investigated from different perspectives and with different aims. Automatic text retrieval on historical data suffers severely from spelling variation and a common approach to this problem is not to modernise the full text collection, but to expand the search query to cover lexical variants (e.g. (Koolen et al., 2006; Hauser and Schulz, 2007; Ernst-Gerlach and Fuhr, 2007; Gotscharek et al., 2011)). Other approaches attempt to modernise the spelling in the historical documents themselves. The VARD tools were developed for corpus linguistic research into Early Modern English. The original VARD tool consisted of a list of manually created mappings between historical variants and their modern versions. VARD2 (Baron and 66 JLCL From old texts to modern spellings Rayson, 2008) has an additional module that can search for variants and mappings. In the work of Rayson et al. (2005) the ability of VARD to detect spelling variants and suggest the correct modern spelling is compared with two commercial spelling correctors, MS-Word and Aspell, showing that VARD works better for historical texts since it detects fewer false positives. VARD2 will be discussed in more detail in Section 3.1. Kestemont et al. (2010) describe an automatic approach to normalise the spelling in Middle Dutch Text from the 12th century. In this case however, they chose not to convert historical word forms to their modern counterparts, but to their modern lemma. They used machine learning to discover how to transform one spelling variant into another to resolve intra-lemma variation. Several studies of spelling variation in Portuguese historical documents have been conducted and we were grateful to be able to re-use some of the resources already developed for historical Portuguese. We will briefly discuss this previous work and, in Section 3.1, explain how we used the available resources. The Historical Dictionary of Brazilian Portuguese (HDBP) is constructed on the basis of a historical Portuguese corpus of 1,733 texts and approximately 5 million tokens. As there was no standard spelling at the time (16th to 19th century), it is not easy to create lexicographic entries on the basis of the corpus or produce reliable frequency counts. A rule-based method was therefore developed for the automatic detection of spelling variation in the corpus (Giusti et al., 2007). The HDBP researchers compared their automatic variant detection method with Agrep1 using a small test set and showed that their method was more precise, whereas Agrep had much better recall. These experiments also led to a spelling variants dictionary containing approximately 30K clusters of variants. Another resource available for Portuguese is the Digital Corpus of Medieval Portuguese (CIPM- Corpus Informatizado do Português Medieval)2 which covers the 12th to the 16th century and counts around 2 million words. Rocio et al. (2003) describe how they annotated part of the CIPM with linguistic information such as POS-tags, morphological analysis and partial parse information. They did not proceed with modernisation but used automatic tools on the historical data as such, followed by a manual correction phase. The Tycho Brahe Parsed Corpus of Historical Portuguese3 is an electronic corpus of historical texts with prose from different text genres from the Middle Ages to the Late Modern era. The TBCHP contains 52 source texts but not all of them are annotated in the same way. Some of the texts maintain the original spelling variations, while in other texts, intended for part-of-speech and syntactic annotation, the spelling was standardised. 1Agrep: http://www.tgries.de/agrep/ 2CIPM corpus: http://cipm.fcsh.unl.pt/ 3TBCHP: http://www.tycho.iel.unicamp.br/∼tycho/corpus/en JLCL 2011 – Band 26 (2) 67 Hendrickx, Marquilhas 3 Data and Methods This section describes the adaptation of the VARD2 spelling standardisation tool for use with Portuguese, the corpus in question, which is a historical corpus of private letters written in Portuguese, and the experimental setup for normalisation and POS-tagging. 3.1 VARD2 for Portuguese The aim of this study was to evaluate the performance and usefulness of the VARD2 tool for historical Portuguese. As mentioned in Section 2, VARD2 was developed for historical forms of English. It combines several resources to detect and replace spelling variants with a normalised form and, where possible, we adapted every module for use with the Portuguese language. VARD2 uses a modern lexicon, a spelling variants dictionary list that matches variants against their modern counterparts, a list of letter replacement rules and a phonetic matching algorithm to predict normalised candidates for each variant detected, using an edit distance algorithm to determine the most likely candidate. The tool can be configured, since each module can be assigned a certain weight and can be individually configured in favour of recall or precision. When training the tools on a specific data set, new words and variants are added to the lists and the module weights are adapted accordingly.
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